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Record W3045011296 · doi:10.1109/ojpel.2020.3010830

Buck-Plus-Unfolder as the Superior Active Power Decoupling Solution for 400 Vdc/kW-Level Applications

2020· article· en· W3045011296 on OpenAlexafffund
Sina Sadrian, Jiacheng Wang

Bibliographic record

VenueIEEE Open Journal of Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDecoupling (probability)Computer scienceBuck converterElectronic circuitElectronic engineeringGranularityReliability (semiconductor)CapacitanceModulation (music)Power (physics)Topology (electrical circuits)Electrical engineeringVoltageEngineeringControl engineeringPhysics

Abstract

fetched live from OpenAlex

In single-phase ac/dc applications where reliability and/or power-density are critical, active power decoupling (APD) circuits can be employed to reduce the required capacitance on the dc-link. Various APD circuits have been proposed so far, all with their advantages and disadvantages. However, many confusions still exist in the literature on this topic which is mainly attributed to a lack of unified and comprehensive assessment criteria. In this paper, first the decisive criteria for a modern APD circuit are established, and the buck APD is identified as the current state-of-the-art, based on them. Then the buck-plus-unfolder topology with triangular current mode (TCM) modulation is proposed as an improvement, and a simple, yet solid foundation is introduced to choose the superior decoupling solution at different specifications. The operation equations for the APD with TCM modulation are derived next, and the operation of the proposed solution is demonstrated using a hardware prototype.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.282
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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